An Integrated Framework for More Efficient Web Services Selection Using an Improved Fuzzy AHP

Abstract

With the rapid development of Cloud Computing and Service Oriented Computing, the process of selecting web services which gives the same functionality with different quality of service (QoS) become an important issue. To deal with the large number of Web services candidates, K-representative Skyline is appeared as a Skyline variant to find the short list of the most relevant Web services that represent a summary about the full skyline Web services result. However, it returns generally a conflicting result. To rank-order K-representative Skyline Web Services, we propose an improved Fuzzy AHP which allows to: i) elicit the QoS importance level using linguistic terms based on natural language, asking fewer efforts to users, ii) reduce the number of inputs and generate automatically all pair-wise matrix of Skyline Web services with respect to each attribute. The experimental evaluation conducted on real world dataset illustrates the feasibility and the effectiveness of our framework in comparison with existing works.